A forest inventory is an estimate or census of a population parameter in or about a forest. Parties often pay for forest inventories of marketable forest products like standing timber volume and biomass/carbon.
Methods of inventorying standing timber may either be an estimate which predicts the population parameter total from a sample of the population, or a census, which measures all elements within the given population. Inventories are used to assess the value of the population and to develop management plans that mean to augment the population development trajectory in such a way that they will become more valuable.
Inventories must yield enough information of sufficient quality to make educated decisions, without becoming too costly such that the costs outweigh the foreseeable benefits. Inventories are therefore evaluated based on the quantity, quality, and cost of the information they provide. Quantity refers to the number of population parameters the information describes. For standing timber, the most common parameters are the number, size, and species of trees in the population. Since management of standing timber is planned at the management unit (stand) level, estimated information on individual trees is often summarized in tables that describe the stand. Quality most often refers to the amount of confidence that can be placed in the estimation to be within some % of the true value of the population parameter. The most common in the field of forest inventory is to be 90% confident that the estimate is within 10% of the true value. An increase in the desired quality typically necessitates greater sampling intensity, and therefore results in increased costs.
Several methods have been developed to create forest inventories of standing timber with the aim of reducing costs. General categories of methods, which are described in greater detail below, include: ground cruising, spectral, radar, and lidar plus spectral. Ground cruising involves taking a given population, such as a stand, estimating the internal variation that exists within the stand for a given parameter (usually basal-area which is the cross sectional area in an acre that is covered by tree stems), and solving the number of samples of a given size that are needed to achieve the desired quality of estimate. Measurements of parameters are collected in plots or points within the stand and aggregated to provide an estimate of the population or population mean.
Spectral methods (meaning passively collected images of reflected light in partitioned spectral bands from the ground) include the use of aerial photographs or satellite imagery to improve estimates and reduce the total cost of inventory. There are 3 ways of doing this:
1) Use images to either manually or automatically partition stands such that the internal variation per stand is minimized, therefore reducing the total number of plots required across multiple stands to achieve the desired inventory quality.
2) Correlate the reflectance values in a given pixel (across multiple spectral bands) to the population parameter measurements. The correlated values are then used to extrapolate an stand level estimation. This can be useful in producing forest type maps indicating a dominant species or set of species or harvested v. non-harvested lands.
3) Time series images may be used to asses between and among pixel variance to determine parameters such as age which occasionally correlate well to basal area.
Radar in various forms has been used to estimate the total basal area and qualitative measures of structure, but has largely been overlooked due to poor direct correlations between basal area and return values. More common is the integration of radar in analytical methods used to determine land use type (forest v. non-forest).
Lidar uses lasers to accurately measure the height of objects in 3 dimensional space. Individual trees are identified (with varying degrees of success). Height is correlated to diameter to characterize the volume of each tree. Lidar analysis is therefore a type of census. If combined with high resolution spectral imagery and a method of segmenting canopy space for each tree, it is theoretically possible to identify the species of each tree. Though there may be errors in segmentation, the result is a list of every identified tree's species and size in the population. The drawback is that this method is often more costly to deploy than the market will bear.
In view of the foregoing, it would be desirable to provide systems and methods for high accuracy, cost-effective forest inventory assessment.